{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.18.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['False_',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__array_api_version__',\n",
       " '__array_namespace_info__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_attributes__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__numpy_submodules__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_array_api_info',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_expired_attrs_2_0',\n",
       " '_get_promotion_state',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_msg',\n",
       " '_no_nep50_warning',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_set_promotion_state',\n",
       " '_specific_msg',\n",
       " '_type_info',\n",
       " '_typing',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'acos',\n",
       " 'acosh',\n",
       " 'add',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfortranarray',\n",
       " 'asin',\n",
       " 'asinh',\n",
       " 'asmatrix',\n",
       " 'astype',\n",
       " 'atan',\n",
       " 'atan2',\n",
       " 'atanh',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_count',\n",
       " 'bitwise_invert',\n",
       " 'bitwise_left_shift',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_right_shift',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'char',\n",
       " 'character',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concat',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'core',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'cumulative_prod',\n",
       " 'cumulative_sum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'f2py',\n",
       " 'fabs',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isdtype',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issubdtype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'long',\n",
       " 'longdouble',\n",
       " 'longlong',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'matrix_transpose',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'number',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'permute_dims',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'pow',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctypeDict',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_printoptions',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'strings',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'transpose',\n",
       " 'trapezoid',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulong',\n",
       " 'ulonglong',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unique_all',\n",
       " 'unique_counts',\n",
       " 'unique_inverse',\n",
       " 'unique_values',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unstack',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vecdot',\n",
       " 'vectorize',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date  open  high   low  close  pre_close  change  \\\n",
      "0     601939.SH   20241225  8.78  9.02  8.77   8.88       8.78    0.10   \n",
      "1     601939.SH   20241224  8.60  8.78  8.59   8.78       8.62    0.16   \n",
      "2     601939.SH   20241223  8.45  8.73  8.45   8.62       8.48    0.14   \n",
      "3     601939.SH   20241220  8.47  8.60  8.46   8.48       8.48    0.00   \n",
      "4     601939.SH   20241219  8.52  8.58  8.40   8.48       8.55   -0.07   \n",
      "...         ...        ...   ...   ...   ...    ...        ...     ...   \n",
      "3622  601939.SH   20100108  6.01  6.08  5.99   6.04       6.02    0.02   \n",
      "3623  601939.SH   20100107  6.11  6.15  6.01   6.02       6.11   -0.09   \n",
      "3624  601939.SH   20100106  6.20  6.23  6.10   6.11       6.21   -0.10   \n",
      "3625  601939.SH   20100105  6.15  6.26  6.08   6.21       6.12    0.09   \n",
      "3626  601939.SH   20100104  6.21  6.27  6.12   6.12       6.19   -0.07   \n",
      "\n",
      "      pct_chg         vol       amount  \n",
      "0      1.1390  1453468.04  1290884.287  \n",
      "1      1.8561  1294148.26  1128814.466  \n",
      "2      1.6509  1499687.80  1294586.538  \n",
      "3      0.0000   801627.61   682584.497  \n",
      "4     -0.8187   902493.13   765971.927  \n",
      "...       ...         ...          ...  \n",
      "3622   0.3300   564801.55   340188.391  \n",
      "3623  -1.4700  1212983.59   736255.989  \n",
      "3624  -1.6100  1060383.44   654623.387  \n",
      "3625   1.4700  1230221.90   761187.887  \n",
      "3626  -1.1300  1139845.17   705783.462  \n",
      "\n",
      "[3627 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('01f9566af0d21fcf69e4ef50d37a2e4f5e79ba928b150b2e2f5288da')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"601939.SH\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date  open  high   low  close  pre_close  change  \\\n",
      "0     601939.SH   20241225  8.78  9.02  8.77   8.88       8.78    0.10   \n",
      "1     601939.SH   20241224  8.60  8.78  8.59   8.78       8.62    0.16   \n",
      "2     601939.SH   20241223  8.45  8.73  8.45   8.62       8.48    0.14   \n",
      "3     601939.SH   20241220  8.47  8.60  8.46   8.48       8.48    0.00   \n",
      "4     601939.SH   20241219  8.52  8.58  8.40   8.48       8.55   -0.07   \n",
      "...         ...        ...   ...   ...   ...    ...        ...     ...   \n",
      "3622  601939.SH   20100108  6.01  6.08  5.99   6.04       6.02    0.02   \n",
      "3623  601939.SH   20100107  6.11  6.15  6.01   6.02       6.11   -0.09   \n",
      "3624  601939.SH   20100106  6.20  6.23  6.10   6.11       6.21   -0.10   \n",
      "3625  601939.SH   20100105  6.15  6.26  6.08   6.21       6.12    0.09   \n",
      "3626  601939.SH   20100104  6.21  6.27  6.12   6.12       6.19   -0.07   \n",
      "\n",
      "      pct_chg         vol       amount  \n",
      "0      1.1390  1453468.04  1290884.287  \n",
      "1      1.8561  1294148.26  1128814.466  \n",
      "2      1.6509  1499687.80  1294586.538  \n",
      "3      0.0000   801627.61   682584.497  \n",
      "4     -0.8187   902493.13   765971.927  \n",
      "...       ...         ...          ...  \n",
      "3622   0.3300   564801.55   340188.391  \n",
      "3623  -1.4700  1212983.59   736255.989  \n",
      "3624  -1.6100  1060383.44   654623.387  \n",
      "3625   1.4700  1230221.90   761187.887  \n",
      "3626  -1.1300  1139845.17   705783.462  \n",
      "\n",
      "[3627 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"stock601939.csv\")\n",
    "stockname='建设银行'\n",
    "stockcode='601939'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3617</th>\n",
       "      <th>3618</th>\n",
       "      <th>3619</th>\n",
       "      <th>3620</th>\n",
       "      <th>3621</th>\n",
       "      <th>3622</th>\n",
       "      <th>3623</th>\n",
       "      <th>3624</th>\n",
       "      <th>3625</th>\n",
       "      <th>3626</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>...</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100115</td>\n",
       "      <td>20100114</td>\n",
       "      <td>20100113</td>\n",
       "      <td>20100112</td>\n",
       "      <td>20100111</td>\n",
       "      <td>20100108</td>\n",
       "      <td>20100107</td>\n",
       "      <td>20100106</td>\n",
       "      <td>20100105</td>\n",
       "      <td>20100104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>8.78</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.31</td>\n",
       "      <td>8.29</td>\n",
       "      <td>8.22</td>\n",
       "      <td>...</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.63</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>9.02</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.65</td>\n",
       "      <td>8.57</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.39</td>\n",
       "      <td>8.34</td>\n",
       "      <td>...</td>\n",
       "      <td>5.91</td>\n",
       "      <td>5.94</td>\n",
       "      <td>6.07</td>\n",
       "      <td>6.18</td>\n",
       "      <td>6.63</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>8.77</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.49</td>\n",
       "      <td>8.44</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.26</td>\n",
       "      <td>8.18</td>\n",
       "      <td>...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.85</td>\n",
       "      <td>5.85</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>8.88</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.49</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.31</td>\n",
       "      <td>...</td>\n",
       "      <td>5.88</td>\n",
       "      <td>5.89</td>\n",
       "      <td>5.86</td>\n",
       "      <td>6.17</td>\n",
       "      <td>6.14</td>\n",
       "      <td>6.04</td>\n",
       "      <td>6.02</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>8.78</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.49</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.31</td>\n",
       "      <td>8.21</td>\n",
       "      <td>...</td>\n",
       "      <td>5.89</td>\n",
       "      <td>5.86</td>\n",
       "      <td>6.17</td>\n",
       "      <td>6.14</td>\n",
       "      <td>6.04</td>\n",
       "      <td>6.02</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>1.139</td>\n",
       "      <td>1.8561</td>\n",
       "      <td>1.6509</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.8187</td>\n",
       "      <td>0.7067</td>\n",
       "      <td>0.4734</td>\n",
       "      <td>1.8072</td>\n",
       "      <td>-0.1203</td>\n",
       "      <td>1.218</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.51</td>\n",
       "      <td>-5.02</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.33</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>1.47</td>\n",
       "      <td>-1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>1453468.04</td>\n",
       "      <td>1294148.26</td>\n",
       "      <td>1499687.8</td>\n",
       "      <td>801627.61</td>\n",
       "      <td>902493.13</td>\n",
       "      <td>1002938.34</td>\n",
       "      <td>887937.59</td>\n",
       "      <td>1239263.79</td>\n",
       "      <td>1138958.08</td>\n",
       "      <td>1088774.79</td>\n",
       "      <td>...</td>\n",
       "      <td>983743.58</td>\n",
       "      <td>1113058.27</td>\n",
       "      <td>2004459.18</td>\n",
       "      <td>1611011.98</td>\n",
       "      <td>2351778.34</td>\n",
       "      <td>564801.55</td>\n",
       "      <td>1212983.59</td>\n",
       "      <td>1060383.44</td>\n",
       "      <td>1230221.9</td>\n",
       "      <td>1139845.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>1290884.287</td>\n",
       "      <td>1128814.466</td>\n",
       "      <td>1294586.538</td>\n",
       "      <td>682584.497</td>\n",
       "      <td>765971.927</td>\n",
       "      <td>859273.151</td>\n",
       "      <td>755171.01</td>\n",
       "      <td>1045535.19</td>\n",
       "      <td>948661.065</td>\n",
       "      <td>900918.867</td>\n",
       "      <td>...</td>\n",
       "      <td>577423.374</td>\n",
       "      <td>655157.343</td>\n",
       "      <td>1193187.793</td>\n",
       "      <td>979282.765</td>\n",
       "      <td>1475780.887</td>\n",
       "      <td>340188.391</td>\n",
       "      <td>736255.989</td>\n",
       "      <td>654623.387</td>\n",
       "      <td>761187.887</td>\n",
       "      <td>705783.462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3627 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0            1            2           3           4     \\\n",
       "ts_code       601939.SH    601939.SH    601939.SH   601939.SH   601939.SH   \n",
       "trade_date     20241225     20241224     20241223    20241220    20241219   \n",
       "open               8.78          8.6         8.45        8.47        8.52   \n",
       "high               9.02         8.78         8.73         8.6        8.58   \n",
       "low                8.77         8.59         8.45        8.46         8.4   \n",
       "close              8.88         8.78         8.62        8.48        8.48   \n",
       "pre_close          8.78         8.62         8.48        8.48        8.55   \n",
       "change              0.1         0.16         0.14         0.0       -0.07   \n",
       "pct_chg           1.139       1.8561       1.6509         0.0     -0.8187   \n",
       "vol          1453468.04   1294148.26    1499687.8   801627.61   902493.13   \n",
       "amount      1290884.287  1128814.466  1294586.538  682584.497  765971.927   \n",
       "\n",
       "                  5          6           7           8           9     ...  \\\n",
       "ts_code      601939.SH  601939.SH   601939.SH   601939.SH   601939.SH  ...   \n",
       "trade_date    20241218   20241217    20241216    20241213    20241212  ...   \n",
       "open              8.52       8.45        8.31        8.29        8.22  ...   \n",
       "high              8.65       8.57        8.55        8.39        8.34  ...   \n",
       "low               8.49       8.44         8.3        8.26        8.18  ...   \n",
       "close             8.55       8.49        8.45         8.3        8.31  ...   \n",
       "pre_close         8.49       8.45         8.3        8.31        8.21  ...   \n",
       "change            0.06       0.04        0.15       -0.01         0.1  ...   \n",
       "pct_chg         0.7067     0.4734      1.8072     -0.1203       1.218  ...   \n",
       "vol         1002938.34  887937.59  1239263.79  1138958.08  1088774.79  ...   \n",
       "amount      859273.151  755171.01  1045535.19  948661.065  900918.867  ...   \n",
       "\n",
       "                  3617        3618         3619        3620         3621  \\\n",
       "ts_code      601939.SH   601939.SH    601939.SH   601939.SH    601939.SH   \n",
       "trade_date    20100115    20100114     20100113    20100112     20100111   \n",
       "open               5.9         5.9          6.0         6.1         6.63   \n",
       "high              5.91        5.94         6.07        6.18         6.63   \n",
       "low                5.8        5.85         5.85        5.99         6.08   \n",
       "close             5.88        5.89         5.86        6.17         6.14   \n",
       "pre_close         5.89        5.86         6.17        6.14         6.04   \n",
       "change           -0.01        0.03        -0.31        0.03          0.1   \n",
       "pct_chg          -0.17        0.51        -5.02        0.49         1.66   \n",
       "vol          983743.58  1113058.27   2004459.18  1611011.98   2351778.34   \n",
       "amount      577423.374  655157.343  1193187.793  979282.765  1475780.887   \n",
       "\n",
       "                  3622        3623        3624        3625        3626  \n",
       "ts_code      601939.SH   601939.SH   601939.SH   601939.SH   601939.SH  \n",
       "trade_date    20100108    20100107    20100106    20100105    20100104  \n",
       "open              6.01        6.11         6.2        6.15        6.21  \n",
       "high              6.08        6.15        6.23        6.26        6.27  \n",
       "low               5.99        6.01         6.1        6.08        6.12  \n",
       "close             6.04        6.02        6.11        6.21        6.12  \n",
       "pre_close         6.02        6.11        6.21        6.12        6.19  \n",
       "change            0.02       -0.09        -0.1        0.09       -0.07  \n",
       "pct_chg           0.33       -1.47       -1.61        1.47       -1.13  \n",
       "vol          564801.55  1212983.59  1060383.44   1230221.9  1139845.17  \n",
       "amount      340188.391  736255.989  654623.387  761187.887  705783.462  \n",
       "\n",
       "[11 rows x 3627 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x29a204752a0>]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NyVs2UCh5NkaPHo277roLL7/8Murq6vDWW2+hrq4Ow4YN8+P0RFSs4rNRDC2hEZuuVjWSuI2okJmSeqWw6RbBlcMlX4KN7bffHosXL8b06dOxatUqDBs2DH//+99RWupbzjAiKkbaGC5jsDHAZqBbETXBEwGw5NlIorwcQimQlg0hBA455BAccsghfpyOiCiqM5ZLwPDFRfTpDwwdDiyar+/HYIOKjdtgA+EYy5T7cIeIyElbCwBAVPc0bRYbbWLej8EGFRvr2CUn5RXBlsMlBhtEFF6R2CDzEksjrHXmCYMNKjbJ0pMPHKw/7tU7+LK4wGCDiMJLG/iZ0OfMYIOKnOrwmR86HMqZl8afit+GY3gDR3ASUXhp0+etLRls2aBi59CyoVx0I0R5BZS7pwGNS4AhG2a3XA4YbBBRKMmuLqhTH4o+sbZsJCTwYrBBRcYhj5WIjdEQZeXRgdQhwW4UIgqnb2fpj0WKYEMCcuHPkKubgy8XAKmqkPO+g+xoz8r7ESXIs9Y8BhtEFE7GqX0pulHkgh+hXnMO1JsuyULBoitsqpMuhHrLZVl5PyIg2tonV8fWBmKwQUSUOWlclNF6YVVKzPu+/d/og+XZWZRNvv9G9MHP32fl/YgAQL32PKjnHwfZuDj5bJQQYrBBROHUmaSLosQcbGB9S/yh/GluQAUiyrHYGkDy8w/YskFE5IuODufXkuTdkCubAiqQ/fsRZYP8/hv9iVLiPqlXSDDYIKJwMg6+tC6+lmytBw7apAKkPv+4/kRRkrf8hRCDDSIKJ8MAUfnFR+bXEgaMGh4z2KBCtGaV/ri8HGjPr885gw0iCr+uTvPzZN0Y7W3BloUoF4xBdEmJh4XYwoHBBhGFU7Lxb5bZKCbd3c6vEeUrY7DhkNALNb2yU5Y0MNggonBKNrXPOmbDuE6EdXxHEBbMC/49iIw69QHT8sl7za+VRgdMi532zmaJPGGwQUThZPz2VlFlfs3ajdKqT33Nt1H6YSbb2yAXzYfMs2mWRUf7W1HCO0uKwQYRhZPxBqdaAohks1F61gRTHqNefYJ/jxBQrzkH6tVnA7Nnpd6Zcie+YGF4b+nhLRkRFTdjy0Z1D/NryYKNZOM5fCI2Hm27XUYikFJCFkrrSiwjq/z0vRwXhNwJb8sGV30lolCSb74Qf1xylmUNkmTf4JwGz/nJJtiRXV1Q/3EKsHIF0Ks3lKvvhrAGSUTp2mxL4Nsvku/DbhQiovSJDUeaNyRr2bB2uQTBLtj5eW400ACANasgv/wocZ98xSEbuVdalnqfEGe2ZbBBRPkn2UU1GwtU2X2DtLaopLg5yEgEcuHPkNloiUmDbGvNdRHIyE3XHMdsEJFGtq43r2hKSVVut2vixqQtG1m4ebu4qIvS5L3U8sl7oF5zDuTLT/tVKl+pl56S6yKQkZsp3WzZICIAkGtXQT37CKhXnJHrooTfiE0BAD32OSDxtZyP2bC5qFumh8qu5Bke5czXo/+//IxvxfLVujW5LgEZuWrZYLBBRADkt19GH8RG+VMSsZu3sLuAJruoBhxsyPZWyP+9pT9vXKw9Mu/Y6bxqrVzVrD/JwuwZKgBs2SAiCoAWNJQk3oxFRUWS44IdICqffdT8dpedHnvBsmOSBeHUGy/Sn5TkwWU4WbcVZYebrleO2SAi8kjrlrC70Y0e73xc0C0bTtMPLQNT5TMPQp36kP2+K5bpj0vyIAOBm5kQFCybbhSx9c7mDZz6SkTkkdZCYfNtTVT3iI/pSDwumGBDro+lRK+ts9/BJmiQb7wAueCn5CfOh26UFINdKQss3SjKZbdCbGsZPM1uFKLiJlc2QX3hKWDt6lwXJdTk6maoLzwZHdMQazYWTt+qreulaAIINtQX/w/qOUdC/eBtoKbO07Hyiw+T7xDCLgr1/TfMG9iykXvGJeUHbwAxbERiF1yIu1EYrhJlgXrL5cCyJbkuRuip904CfpoL+cVH8YurKLO/0YnBwyDn2HRpBJBnQ770f9H/p9wGscOe9jmunGYLlJWbzzX3a/PrIbyRy0fvMD//8G3I3x0K0aNnjkpUPOS6NZBffgyx7a4QlYaA2jC7SWyxXfSBtVUsvA0bbNkgygoGGu78NDf6/5IFesuGU7Ax4U/25wh6zEaXwywTp4GpdX3Nu935T/Pr+dBFsW4N1HOOhPzy41yXpOCpd18P+didicvId3UCAMT+B0NMODy6LSHYCO8tPbwlI6LipjUbW1oGNKK6p32rQNB5Nhb9Yr/dKdiwToGN3TTiDHWQ7a3oXvZr+mXzSDYugbSWJwn1rmsDLA0BAObNAQDIj2aYt2vBxm776QG4daZWiMf/MNggopyT7a2QUpqDhxQtG7Ej44/EgUdFHwS94upy+2BAfjLTfn9rfgRrMFTTS9/1stOx9IQDILOQh0V+/w3Uy06Deuvlgb8XZUZKqQepxuDbGlyUJ5kSnmMMNogop+SCeVDPOhxyym3mi6X2TS7ZmAZj1s7YsfKbT92/d+MSqC8/420dkN797M/1qUOwkaqlpapafxxL9iVnz3JfnjRJbRDoj3MCfy/KUFen/lmvrNS3WweIMtggIrKnvjINACA/nAGUG761xQeI2nejAIY8AxuOBHrHxkZ4uOCqV50F+cKTkP9+2H2B6/okbJIyybKoqQasxuopv/lc3+YmW2TG0htNKINuOSp2dmN42tv0x+WGYMPSsiHKnf9Wci0PRiYRUdFYvTJxW2kp0GG/zog4+nRgk3EQW+4IdHdFO1XWrEr5NrKpEehoi3fVyB++dV9Gu5aKZNkdU7VsNC6BXLkC6u1X6dvSuKHLH2ZDfeo+KEeeCjFqMxcHpDm2ZWUT0L8+vWMpPVo22opKCONUaWtuF7ZsEBHZEym+YSdt2aiqhrLbfhA1tfp4j0gEkUkXQXXq1gCgXvIXqFedrW/wcuO127etxXl/a7BhvVE3LwcWWhJ/dSdfxM32bW67EliyAOpNF7s7IFlrTDJZaXUpYjbBaXy6tDUFvrUbJcnfSq4x2CAKgIxEPI3yJ2fC7bc1Y/PzvDmQ999ku5vt4EsvN167Vox1a533t948tAykfQfou1hmeUhjs7lbXj9v6c7acbNGB6XP5rMoH7vTfl/rANEQp75nsEEUAPXWy6Gee4ye4pqcJUux3KsPhM1CbLZc7qfefrXNRg83XrubunE59q12NL0kp/8b0thS0RGdCit+d6jze2RhXIRMtxsljVYXsielhPrWy5DGQbqWYMOUzdXaKmYNNrg2ClGR+f4boKMNcvbnqfctdsmSWvWxn/lhy23ab7sEa83L3b9PLJOjmHhMPGW61Fo26vpADBhk3r+7C/Ld1wzHx2bZ9BsAJ/Kn79yXR+N1XQy2bOTe159BPn2/eRVgA/Xjd03ZXIUlkEVFpfk510YhKlIBpM4uOMm6SbwkKcowe2LSGSXGMq6OTk9FaRlQFguUWmLBRmU15A+zE4//daH+WMtAWp0k9ffP30N2eWxB8LrGikN9E9KpW3HMhm9STdOWD95s3lBimQbes8b8fNAGPpQqGAw2iAIkn7g3+U2Mkg9qsw6AS8btzdYpKOlMMuZho00St5WV6YNS16+L/l9RaZ6mqKmogPrQLVBn/Fd/n7IUY1HW2MzMScbr58yhZUN1GOsSx7FIvpEzX9cfz/og9QGWz7h18LQIcep7BhtEQepoA+b/kOtShIr88iOozzwEGbvZybdectxXeBjwJtw2ITtlJO1st98OJM4CAGItG7GLvZYUrKzMfjbB4l8gP3oH8qn79MAkaWZUJB90asdrsOHUQpHq59jhsDYMeSZ23DP+WL3nhtRfTJJ9RkOOwQaRzxIuGHY3KgBSW3SsyKh3XQf55gtQ77gm9c5BrPXgdJN1mAGivvWyfcBYamjZaFsf/b+s3P6m/91X+mPt9fLyhGZw5VS9714uXWRfTidegw2n/YdtZL89VlbZkcZMGbJnbdVLNR7GS6bbkGGwQeQ36zgNp2+KxhkMxWj254hceELyfbx0o7hlHcCpsS6YFiOfvt9+/9JSfXCrFqiUlrm/6ZdVQDnNkhPDUAY55TZ350mXQzeK6OMwcLVnbfT/dKblkj3V8llZ1ZR8f4cvLvnAtw6ed955B3fffXfC9tNPPx177LGHX29DFH7Wi3iIR4jn3MoVyV+3DojzgdhkrH2rgc2FXH7xkfN5SssgYy0bMt6yUeZ+UHB5BcSosVCuvgvq5WfEypDFG7khKJJSGrqhHIKlHrFWmDy+4YWP5We9vDH53jbTjsXJf4ecMhnKyX/3s2C+8y3Y2GWXXbDtttvGn7e3t+OCCy7Apptu6tdbEOWHiPVmw2AjbRmmX5aRSGKeDqeWB5ubqHr3dc4nLy3Tx13EmrdFaZnrAcHaaraiYSgwbASw8CeIzbdF9bIlaH3zJWDs1q7OkzbVkMtDVfU8JQ7lF3V9o7dGtmz4Qv40F/K918zbViUPvpV9Dkzctt1ukFvtFOrBoYCPwUZpaSlKDZV97bXXsN1226G+njn0qchIS0ImAaBXb5s1OzhLJSW3Cb2cdHaYV1UFnH/sdt/Y+/SPrgVip7RUH7PRamzZ8P57VS6+CWhrhajthYqxW0aDjaBbxIwtcN1djsGG2G1/oFednmisvS0aUHW0QVRafrYFRkoJ+eL/QWw4EmKLbVMf4IF6wwWJ7/fCk477K9fcDVE/xPa1sAcaQEBjNjo7O/HKK69g4sSJQZyeKNwS+sKF/XRLa38tJZAfvOVpf+Xcq8wburuiS9i/+6qhxcEhv4RdsDE4Sd6C0jJ9KqLWLVNW7i7pVSwZmEaUlkbXdwGgaK05DmNIAmEsc+znJPY5EMo1d0M55nQoBxwJVMbK3NEG9S8HQj3r8MIf5PzVJ5AvPw31TheDmf1g+EKiXH2XaQCpU6CRLwIJh95//31svPHGGDDAfqBRV1cXugwJa4QQqKqqij/2i3YuP88ZZqxvSFi/GQphnwPC1E/uTmjrHDDX9d1knPm47m5E/nle9HGv3hBb7uD8Hp0dCe8jpOrYECLKyoDZs8wbu7uBiH5tU44/G+qUyYkH9+5jWychhN511NWZ9u/Z1XGGfUSkWz9GCzZq66A0DNX3qayCBCBnfRjfpt5wAUofdJ667LacYf08y7X6zd+PMnqprzJoGJR7noU6exZE//rQ/ozcCiTYeOONN3Dooc55/59//nlMmzYt/nz48OGYNGkS+vfvH0Rxiq4rh/XNrcjqCvxqeN63Xz80l5fDutpF7161qG5oSOs9wlZnLzxO6ATgrb7t192DpktOiz4xpIEWzzyIht9NxMqqKqy3Oa62ohw1lt/H8tJSOLUv9GsYhGWWbWUrlkEZNRbtsz6EqKxC/Z7741ebYKPntrugt8Pvvm3J/Oi5IFHv4fNh/Lk2uDhuWVkZtPRcA/r0RumA6DHNVVVoBVDTqxdqDedZP7ABK4GE6Zdu3iuVsH6eW/r2gxZu+FFPTX19fcq/g/j7Nfzet/fNJd+DjcbGRjQ2NmLzzTd33GfixImYMGFC/LkWsTU1NaHbx7z7QgjU19ejsbGxKLI4sr7hILV01jHNzc2I2JRvVXMz1iy1WYE0ibDWOVMll/wLkeucR9N7qu+AIdGcEC3rEFmhhwORFcvw63ezoa63CzWAtU3L0WL5fUTanAdDrli9OmFb96ixELvvD9F3IJTd98PyTvvrWdt+B6Pd5ncvhEBdRbRlo6t1PZZ6/Hxo3BzXbciYunzJEogIINesghpbPHDdunVYbziP2m4TdpWVp11GIPyfZ3W1Pj09k3pqjPVNxY/3y4bS0lJXDQW+BxsffPABtt56a9NgUauysjKUOWTPC+IDJ6UM5Qc5KKxvbknLip1SStsxGzISSbvcYatzpuQGG9tuFxP+FH3da31L7a8vkb8f51wGm9+H9Xdpes0u4djOvwFq66AcFs0fYlvmklKgotKxPvEU1F1dGX0+XOykP1QjkF9+YhqbIDs7TOeRNmNIxA57+PI5DOvn2bgyrp/lS3Uu5dSLQvnzyITvA0S/+uorjBkzxu/TEoWGlBLqe69BLppvv4N1gKiqAo2LbU5UfIu02V1Axb4HQTisayL6pNm16hBsJGdzcVeTLPVuSaUudtkHIsUqteLPZ0K58eHk+xjGbATK+LtQVajTp5pfnvmGef88HzOQllzd8EdvkZv3DZCvwUZnZyd+/PFHbLKJzaJFRAVCfvIe5ON3Qb36bPsdrMGGthZGqv2KgV2d+w0EAIid99a3bbkD0DAUYvvd03ufdIINuxtLkpaNhEWxdt3XdjexnV4HscHGELV1SYshyvWWjUBZgo2EYMKS4VbYLUaX7OdTCAIKNhKC7uGjzM+tS8cXAF+7UcrLy/HUU0/5eUqi8Em1sJrl27B6/40O+xVXsKF+8DbkMw8kvqC1EBi6JZQjToHo3Tf9Efh+BRvJWp8UJT42BHC4GQMQO+8F+cm70ScuFpYT2mqwNtkifWUNNqys9bH7Xfg4xi6Uggo2vrRkptVSwcckJKIrAFwbhcirVM3b1gu307e/Igs25JTb9ORXRvFgw3A5yrTJ3u1y80YuWjZMLS2KAuXCGyH2/gOUSUm6RoaN8FQuvWWjM3v99jYtG8qRp1gKZlN2p0XtCkVAXZ3yi4/NG1KtAFwAwp92jChsUjVvuw0iko0HKCaliS0bUEISbFh/l5WGZFyKAlE/GOLwk5OeVvSshTjkeGDZEqB+cMpixIMNINpyENSNyHgjVSNISKtf08tSMJtTFHywEdB5V5lnrAmlpODzCTPYIPIqVfO222Cj0C/ULsWbjI1rbth9i/Z00jSCFTdjNkzdIO7fQ9nPfTZlYVx2vKszwGDDUF+p2gR4lud2v5NC70YxhADmxeoyPOu8OebnRbACNLtRiDySKVs2XLZYFPrgOrdiN3D59aeGjSFp2Viup2cTx55lWasloO+ixvEm3QHOSDEWX1WR8DO3Bh92N9rZn/tdqnCxBmR+MQaUAMSm4xx2LBwMNoi8YsuGZ3KpzdRfjdaNYpq1k+GNPJ2WEWuOjSY98ZJyyc1QdtkH2HCkvkNAi5AJIfSbUZAzUow3TyltggkXwQYA+f1sf8sVJqZgw8fzWruoDN1zyjmW9X0KBIMNIq+8DhB1UkwtG0uTJWeOrRdx4JH6pkwDsbTGfEjIlrX6oMzm5fpLsdYqsc0u0QXKTrnAMTeIL7Suk452yFhGz0DZfWYTYg+HYGPWB/6XJyxMAZl/LRtisy31x7vsYw5c83zBNScMNoi8YsuGd8mm8sV+nmL/Q/RtVT0ze780Wjbk6/+Beu7RkP+JLfNtHAza0RY7rQLlsBMhttkls/KlEmvZUG+5DOo5R5paWXyTKs+GmzEbAFBZBVmoM6uMrRkeZwYl/Zloi91ttzvEMWdAVBmCjZLCvC0XZq2IgpRqUJzbMRsFP7hOp874r+NrUgs2SkuhXHMPlKvvgoitD5K2DFodpJZJ09jyNCrLfepay0ZsyXH54dv+v4c12LDeTK2tQ4bnYrf99NNM/zfUvx6ReixTPrJ2NbmkPvcY1POOgVxhXapP2yF23vrB0RaycsPn3UUulnzEYIPIK3ajeLf4F+fXDDcpUT8YwrCsedoynDUgmxr1YLB+CESStZ4CUWoeQBhIYGoNNjraLTskaemorIb4wxH684424IcCHLthjC9aHDIB2x32yjRg/TrI156z30H7QqIFxcZrRnWGrXohxWCDyKtULRfsRkmtYSiw8WhACIhx2/h//gzHU6hP3af/fnKRzdHy7Va+Mg2y1eXYDReBlpTSvF6PjADt5qXjE85TVQX0jy0Fbxwoq5/UXfnyiaFlQ73hAneHGLu8rEFj/GSx82qf0x56gFGI2UMB5tkg8p/bgWTF1LLRoybeJQAA4veHQWy7K9DVCRHEOhAexmyIbXeF/HSmeWNnh/77SWtRtwwt+SVhk3z5GYjDTkx9rJtWnWW/mp+rqjnPic15hFIC5aq7gNYWiF69ob5oXppCLvsVYuxWqd87nxgDqFUr3B2zeqX+2Gn8RbxlIxZYjBgN8btDC3ZwKMCWDaI0pLiYuw0iiqhlQ+y+v3mDokAoSjCBBuCtG8XuAi8E5OpYlkfjzSOX1q52t5+bQMs6yFlVowGW6TyJP0NRVgbRq3f08Z4TTK/Jp+93V758Yp0O7Wa9GmMG2EHDzMd3dWLN0w9C/jIvuiHWsiGEgDLxGCg77plRccOMwQaR31x2o8iEPvICZhnz4FcmRkduulE2HAmMHAOx1Q6Jr33/DeRjd0Yfr8lBsFFu0/zutnvOzc/WctOUdgNEUwXVPWvclSefWYONmW9AWoMyK9WYm8Ny/Ov/wdrH79WngiuF2WVih8EGUQbU/72ZuNFtN0pYvjFng2q5kQUdbLg4v3LI8Si54AZknK00CBVVidvcBhsucoyozz+ReG7r+VP8DAMPGMPAGiw8dS/kCylWNjf+/VtaOROmMAeZqyVkiqemRAGQj9yeuM3tTcFts3ghsARg0hp8+M1NV0Isd0YoV9+1mfor3Qaxbuo+5wvryRODZK/BREEObEz8nJrT6tsdYpnlYyCGjzLvW6A5NewUT02J/GIdSGfldsxGESy+FBd0cGHhKrunNkhPm2ERJiU2g1JdB0VptDhYWjbEDnu6mxVh/NmN29b7+4adbWbVFD9fU7BhuRZYrw2ZLjiYR4qnpkQ+kN98DqxuTr6T25tCqr7fQiINF9nNtjSlaw6Em2/lY6NTbkVVNZQbp0C54cFgy+RFJq0E6aRqVyOmXB7ikOPcvdUJ5+hP/FyoLCwiPgcb1gGmRTRmg1NfiTxQn34g9U5uL7pSQqoRiGK44MQCMLH97lBO+lvw7+fiG6MxUZfo3Tex+2vIcGDxfIhslNfKJoukqOvr7tgUN0Np132nqtH31GZI1da5e6+NNtEfF+JUbplGnUxjNiyfKWuwwW4UIrLnojvAyxiAQrxA29G+7WVpQJy0yVORSmLXS2z9iprazAvklV3LxnqXGSxTBVp23YCqCtT1iR5++MmuB38KpQTixHNj5yjAz7Ld33Kqv2/jJcKyr/rcY+Z92Y1CRLbcZEnULjBuLiTF0pWiJfTK1uj7JQvSOkz89uDog34D9UAwF2tV2LxnQuIxJ0nS6cvW9YDNKrLyiw/j3Shi5Gbu3kejtcwVYuBslyY+VVBlbNkwBHbS5rhCzRZqh90oRF64CjYM6x7Y9fkaT/e/tyD2PSjzcoWY7GiHfOOF6JMQfJMTu+4LscX29q9ttRPkK89Gf4cRS5bHbMrkJpQkgFXPPsL+he++0vNmeFwHRpSURL/MF2LLhl3gtnxp8mOM1whjjha7a0ERTX1lsEHkhZeWDTcZQoth+qsxt0COcjOIA4+E/H42lP0PTj441bgwVg7XRhGlpW467BzZjQWSqT678fTsHm8L2vuEcQpxpjpTLLpox9CyIf/3JnDcX6NP7K4HdrOOChSDDSIv3Az+5JgNM8NNTq5bm5sy9G9AyYTDU+9nDDZE7PcYgoXYNOqHMyC23TX1KrRdXUCFpdypVivWVt/12m1UwN0oKbOF2nFqzbT7+dT28n7+PFU8bThEfljpYjEmT8FGEayPYvx5fPlR7srhhjB8S9e6BXISbOjvqZx9RfyxfPhWyM//l/p4u7EGdjdOY6ZSbaZEucf1aspKzccXklQBmh3L37+MRCA72qFef76+sd9AYNRYYIMRGRYwf7Blg8gLRUkdTHjpu95g48zKE3Jy4U9Q77pW39AwNHeFccPYsqF1ZOR6zMZmlpVUly2BXLsqGiisWwM0Lwdqe5v3idjc+O26BCor9UyqmvLE7KVJafsX4mBnuzqlCj6t02W7OiE/egdYtiS+qfSGB6FGIu6SzxUIBhtEXngZs+GCqLJZA6MASCmB7i6o1/7N9PNQDj85h6VyIR5sRIBObcxG9i+TYqe9IT//ABi8QcI0VPnS05AvPZ38BN02Aa/djbPFZjptmcdxBFpLSAeDDQCJf/8d7ZBffZKwWzEFGgCDDSJvfA42XJ0vD6l3XAPM/TrxZ+E2WVSuaDcAYy6KHNwUxObbQrl8MjCgIb0TrGgE+vQzb7O5cYod9ogOYtSUl3u/CRZdy0aK26Y1t8bfj/WxQPmruEIromzwEGwEviBZrnzzmX1/96BhWXl7cdSp5g3zf3B3oF2XSS5mowgBMXQ4REWs1cBt9tAY9aZLEjfa/D7E9rub6+e1CwUAtDJ2tns/NsSklPb5WkpKIZubIGd9aLvoouuFGIsMgw0iv8XGbIgd9nC9byGRK5vsXxg0LHtNx5abpmOZrOzKl4ukXlZpZDGN3H411Bee1DdYZ1JVVEKM3gKoMcyISCfY0I6JRCDtBqa6JJsaEbn+/Gj3URh89bH99pa1UC86Eeo910N+8m7i6ymCjT7n/9OHwuUfBhtEftO+QfbqA4zdKvm+BdiNIr/7yv4Fr/kbMiqE+ecq3HZH2AUbYVi7Jp1vy998BvnyM4ZzWH4me/0++sDYteV1JgoAVBgClAxaN9Sn7gV+/h7qvTfo2958EepT96bOERIAOe+71DvN/TpxW4rfVdX2u6VZovzGYIPIb1qwUV6uNzEDUC69OXHfAgw24vkarLLZQmC54IutdnJ3nG3LRgiCDT8+J9ZzaIFFWblhWzk8KynVf26ZjNv4frbpqYxEIJ95EHLGdCCNtW4yJX+Zl3onu4y4yXLx9K+HUlWdfqHyGIMNIr9pwUZZOUSpPrJfbDgycd+CXJbboWsom8GG4eeqXD4Zwrg6aTJhDTZ+XZj5OaxddlqQYbxhptGNIoTQg+pMZqRYx5Q0L9Mf56J16ftvUu9jnSm0eiXka887719TPEm8rBhsEPmtUw82sPHo5PsW4mAyuxwPAFCZxWm+hp+rGDrc/XF231TD0I2SgfiARWvLhjbF1RhglaXRsgHYzkiRy5dCfvN5WqeT69YAywxrkIS1BdDyeVHPPw5YuijJ/rlJ1x8GIRj5RFRYpKEbReyyD6CqEKPGRrdtOBL45UfDziG9iGbCYZCgyFGw4Ym1jNU9sjvWxCNx1GmQ770KLJrvvFOkG1DKEz9rWpBhvAGmO4BXa8EztE6ol54SPeWlN9u36iUh/zsVGLaRvmHtamDwBumVLUCyKcWibBaiwJP4JcOWDaIMydUrzRviLRsVEEoJlL0mQAzZEACgnHYxxG8O0C+chdiN4jQjIZvdEWkGcUJRgLFbx58r/7g1IalWaJSVQ9njtxDDRyXfTxtDY/2sdcQGcxq7C5wG96ZiyrwaW8pe0+xyJpCBXL7UlP5cveWy9MoVNENW0GSUi26E2OdAKAcXb84NBhtEmVps+VbZHQ02hE0mRtGnH5Q/naQnayrEPBtO673kcICoF8qfz9SfVKQxOyNbYsGbmHhMvDlf/OX8xP1WNEL99xTIpmXm7R02M0fS/blZV35tb9Vfc9uiVT9EfxyJAF3mz5Fctya9sqXBmCtD7LZfxucTIzaFctiJet6UIhTe9kGifGEdEGlo2XCkfRMsxJYNp2Ajq1NfM/i51vWJJrsqKw/3gL7YjUv0rEXJ/f8BAMj5PyQsTa9ec67t4a4Hzbph/TwbW7fcLjZoGEyN8nLId18xvaw+cjtKzspSC4dhCq849ATI916z38/QqiQLcSE6HxV0y4b60tNYeuqhkHb5/4l8Iq0rwRqnvjoQ2sCyQmzZCEE3ijB+S/Z6rBBQTvoblGPPCm8XCmA/mNNleZXTLo4v8CYOOT7zsli6UUwBeBqJ60RFZeJAyx/npFm4NLQZ0tUnaY0QY8brT6wtRT29J2IrZIUdbLzwJLoXzYf65gu5LgoVMPnZTP3x0kV6iuNkI/tjNwX59P2F940oDN0o47aBOOpUKBfdmL33zDIxcrPEjS4HeIqtdowHUsp+E/UX6genWRhrsGH4TDtNhbaqM6xcazcF120LiQ+kIXuoEAJi213td6wwdBG1m4MNU3ccFXawoZEf26SUJfKJ2GjT+GP18jP0F5IFG4abgnzi7qTnl93dkK0taZcv2+SaVfYvZLNlQwgoe/wOYsSmqXfOA2L3/eOPlcsnQ+z5e4hDbVok7KbuuqBcdz/EXhOg/PWK9AqYpGXDdQpzQ9nlzNcTX8/mQm/WpHAn/x3KXf9O2E0+dR/UD2dEnxi7XvadCIzfHuJ3h0E5w2admiJUFMEGmhpzXQIqZG3r7bcn6UYxjtaX/3sr6enVK8+CevaRibNeQkj9+F3gi4/sXwzDGiN5Smy5Y/RBz1qIocOhHHkKhN14kjSnror+9VCO+AtE//r0CmgNNowBRopBpzISgVQjoRq/JOr6RB/EcrQIISDsWltaWyAfvhVSSqjPPaYff8hx0YB34tEQ43fIRpFDj3/9RJlqi468l9YMiMlaNr75zP35Y9Pr5LezgF328Vq6rJJvvuj8YhgyceYpsdmW0S6hgYNS7Jh6zIY40X7AaEa03602PsPlAFG5sgmLT/pD9Ek287CkILWWmeqe5hfGbWP7t6veerlp2nCox/rkSHG0bBD5QNpNFQT0JuP1loHI6awzke+S5bdgy0ZGxIhNIVINOnTRsiE23dynEhlPau1GMQQY7a2QX30CaZPKXH72P8N+bQmvJyM7OyC/+tT57zITWrBkmUGlnHKBeYquJt38JEWEwQaRS+oD/7LdLmPNv/Id81Q9VPUIukjhY7w4xxKZxTW6S4BEGTC0poltdwUG6C0h4sAjodz/AkRdX//fNxbkSJtgQz7zENQ7/wn5pM3YpD790n5L+fQDUO+8BnLK5LTP4UgbtG0JkEVFJZSr7wLGbOl8bN8B/penADDYIHLrq0/st8daNuSsD02bRbrrTOQzQ1eJMuFwKLc/HX8u5/LbX+B61OiPt9wRykl/g9hhTygX3ABlwuHBNe/Hx2zEWvlsuk7khzMSBzpnkK5fG0QqP/9fij3TELFv2QBiXSRJfozCOLuH4nwPNp544gnccMMNfp+WKLziC10ZBriFqP85q4yzIaqqIQzLaYvf/ykHBSoyxpwQnR0Qw0dCOfFciJFjgn3fZEm9DOST95k3uMnBYUg+JmOD/WWACxiqn74P+eS9yXdKFrSxu9CWr8HGggUL8Prrr+P4431IEkOUL7RvZ8YLYDG2agDmxEaxG4ly+9NQzrsGYrd9c1So4mFsuRBDsrhwmfZ5j43LcJruKr80t/6hO3WwIbbfXT/+/TchO9qhnneMvoOxNSdDUlUh7zfkZvnqU6dSOZ+krdX5tSLmWwimqiruv/9+/P73v8fAgQP9Oq1vZOt6iOoi7EOn4GnfzgYOBpbHVoEsotHoMhKJDpDbaBTEkA0htVVtN4kORBRV1cDoLXJYwuKiXD4ZaGrM6gqjot/AaJp07fPvlMir0zJjy02iLkXvmpPTp0JOn2p+vW9/1+VMyTJIVey4p/1+yf6+e/R0fq2I+day8cYbb2DhwoXo378/PvvsM3S7TeSSJerN/8h1EahQxT7rYuud9G2BBBvhDGDkgzdDnXwl1ItOitdbHHCk7UJ0FDwxdDjEVjtm9021gZ6rm6P/fzvL3XFusoumyr9RWZ38dQ/kq9NMz8WfTvR8DmNLDOl8adlob2/H1KlTMWDAAKxYsQIzZ87Es88+i6uuugrlNtP/urq60NWlp7MVQqCqqir+2C8lfz4TkcfujD5Z+FPBz33W6lfo9dSEpr6R7mjSHwjDIljCW7lUFSJFHgohwlNn2d4KlFdAKCWQn70f3djWCsSSlQlF8aWMYalvtuRrfUVVD0gA8pcfETn5gOQ7d3fHA1HpYsyGdm7H14XHv7UkTFNxAYjSMttzCyFsyyR23AuKXfIvw3HG/4uJL8HGxx9/jI6ODlxxxRWora1FJBLB3//+d7z33nv4zW9+k7D/888/j2nT9Ahy+PDhmDRpEvr397E5DEDn1jtgmRZsAGhoaPD1/GFVX59mFsA8la36LnLYXqYoqG9oQEttDbRE3WW9+6A+yefNeq6BNT1R0qsuYT8pJRbHHtf1qkOPWF1z+TuOrFyBX0+KDvYc+t/PTHXRZgbU9OmDWh//3viZDrfWQYPRDOjdKEnUfP0xaiYcCgBYW10N48Lx1bvtCygKWt95Nb5t0AGHYfFDtzier7y8DAN8+qwt6eqEsR2lYfBgCJsBn02VlbDL7tFz8FDUuShLvv1+/eBLsNHc3IyRI0eitjaacKakpATDhg1DY6N9mvCJEydiwoQJ8edalNfU1ORv98tKc3rnpUtT/yHkMyEE6uvr0djYCJnBlLJ8EZb6drW3YenSpVBX6WuCqCec5+nztmz+TxCW7JCRx+6EnK03R69esxrrGhtzXmd1xn/jj3+dZT8deF1rG9b78PcWlt9xtuRrfdV29+uWrFm8AC2xz4a6Wv+bEXv8Fh1/Ognqf81jMhqbmlBy21OInHOk7fk6Ojp8u7arlgRhS5ctt22FiFjHnsS07ro/2pKUJV9/v8mUlpa6aijwJdjo27cvOi0//BUrVmCTTTax3b+srAxlDv25Qf4CCuWXm4qUsmjqCoSgvt3d0TJos1G23AFoGJK0TMpZl0F98GY91fn6dQk5B+R7r5mfS/0znMs6q1/qK2JGLjvddh9ZUuJr+XL+O86yvKtvRYqp3sNHAfN/iD4ur9Drpo132m0/KEedFt1mydAppUw+6NLPn5XNl13bczvNNqvu4aoseff79YEvA0S32morLF68GK+//jqam5sxffp0/PLLL9huu+38OD1RuGkXQm0gm5v1KTbfFsptT8WzbKqTLoT8aW7SY+Qjk6F++0UmJfWHmzIUa56RYpXq923MFFpWEV18bdmvUL/7MrrN0FVhWs7dsF25/wUodxpWXt182+j/ft6zR23mbj8fp9sWC1+CjZqaGlx88cV49913cfbZZ+OVV17Bueeei3790k9F64siHIRD2SNO+pv2KPpfbBqfXR+v7fGKol9MIxGoN1yQ8hj11su9FjMnxNY757oIlE2pUvMbWwzKSqMzmP5xKvDDt9FthsHRwri+i+HmL4SAqKgAYunWRXw6tX/RhojNbBE77Q3lvv8472hddBEAho3wrRyFyLc8G5tuuimuvfZav07njwCzzFGRq+kFUVEZvcxpuQLiizd5mPKZxpLg0s10wRwrylTtxaxn8m/6Yovtot0nn84Eurr0GUwap5YRmxlaypW3A+tbgMXzo39/fnbXabNjho8yBz3W/ZYlrvOjXHyjzZ6kKei8qnLNytQ7EaWjvMLQKmEJNrzkl0gn2Oh0PxiPKCscpnsqZ1waXV9kzHjIH6OtGPLpBxL2E07TRZXEYEP0qAF61EAu/iXt4jr64qPo/6mmoW+7K+S878zbvHzJKEIFHWyImrpcF4EKmbYOhbZ09orY7KvySvv97RRgsKFceUeui0BZ5pg3olcfiOEjAQAy2c14kENqdZtgw/Cm0f99atkwzvxKtb6J2G0/YOkioLY3xOANgNpevpShkBV2sLFh9tL1UpERAqiIfRuLTZeT/3sr+v+vC92fJ9nF1IHssJvhn0VjtwJmz4LYfX/Id19NeFkMzuKaHBRuhlVT5Wr7lubSIRtAbrGt/fElSYJxLb7xKdhQX9ZXKE7ZslFaBqHNniFXimqJ+SBXCqQipH1TW91s/mzNm+P+HA4tG8mmxcmO3LZsCG2a46BhOS0H5QFja8ayX2136XPeVY4tI8lbp32eAGCYDcYxR/4rqmCDA0bJN0KYm1q/0pNbiW138XAehz/BJINAO3+Y7f78AYgPUOVS2pSKoWVDbJA4W0P52z9RscnYhO3ipL8BY8ZDHGifyCu6k7/dKCY17BbxW5EFG+EfxU95xNpEHJv+J3ba2/05nMZsJAk2Vt09yf35g9AdW9eotBTiyFOBAYOi/4iSEPv9MeFzojisBqxsvztKzr0aomdtsjNG/9NWGc5ULOcNAAYbAWCwQZQu04A3CdTELoxexmE4BRtJPqvVu+3r/vw+k7/8GF//QlRVQ9nzdyi59l5gIIMNsmHojhAbjIh+Vnr7lH8poi/mKdtbMz+fsSuHwYbvCj7YqDnseP2J4dui7OyAXLvK5gii1MReE0wtG+juBpqXx1700JfstG/EeY0gkSx1s8/k6pXx2S9ywTyo1/4NWB7re6/Wy6Hsf3D0wVY7Za1sFC6mFr2xW0McdRpEXZ+E/bS05OK3h2T0fnJ9i/6kZV1G5wIA1Nbpj6v8W7aeogo+2Oh1jD5iWH34Nv3xZadD/duxkKuac1Aqyndi5BhzN8rUh/Rg1kuwkUbLRrZmo8iVK6CefxzUa86JPp9nSaderWeNFKM2g3LzY1BOSZ0FlQqT2G+i/ninvaHs8Vv7/bbYFspNj0BMPCazN2w1BBt+LOAZa5EUR56SNKEXpafgf6KmD83Xn0Kd+TrkkoXAyiYAgHrB8Q5HEiUhBFDikDfAadCnHccxG86DmbM2G0VbOKtxCWR7G0Rvy7dUS4pqUVvHi3QxM2QBFakyitb1cc7N4ZYxZbgfWXW1gaYVHvLkkGtFd2WQj90J9cozzduKbPU98oMwd6OYuP88CafxHXbdKLG5/1nLs1FjGJy3vgXS+u2xOnvdOZQHjCnHs3DDFrv/Tn/icTyeVCOQn38AudrQsq3NVvTyZYFc408VAJoac10CyjdCOH8z8xK8Ol3YbKZpi8NPjp4+WxlEjfWIdCc2VVdxZVcyqDSMc0jRsuEHUVOrrybrNdj44G2o994A9epzDBtjf3NsnQsEJ8oDUJ+8FyXnXpXrYlA+SdYC7CXYcMqQqAUbVdUQBx0d/aYYm/2irsnOwGb52f/0J5GIPuU1xrFVhoqSUBSIE88DWtZAZGsqdCwAlp/OhNggdcZo2d0NrGyC/DS2ENy6NfqLK5YBAESK7KGUHgYbQPxDRuReLNrYcGRm8/ydbtjxb1klUPaaAABQP34XAND5/eys/OHKd6brT9SIuY/cr+mLVFCUHfbI7huuXQ0AkK89Dxyij7+TUgKRCISlq1O94QJgwTzTtsjJB0DsvLfewl3BFrsgsL0IMI9qJnJD6/5wWhrbLadvUdqAN2OTrja1NgtM0woBINKtr9ZZ0wvKP27JWlmI3JDL9XTo6h3XQL345MTxTZZAI35sbF0jAJz2GhAGGwDQtj7XJaB8o3Wj2LVMeErq5bCvatN/bJxqG3Tq/Y4283Nj8NHaAmHMSUAUAqZFAb/5DFjdDMz5Un99icsFEi2zrMgfDDYAiGw3/VH+0waHtqxNfM3LADOblg0pJdTH74q9j+Fcxqm2Qc+g6jKPz5Cx5moA/kwzJPJbrPvDlE3UMHBU/c8T7s5TE/zg1mJUFMGGEhvF76hHsvz7RHZiwcbCnxJf0paed8MSmEgpo2OItHEghgunGDNe3zHolg3rOCbDoFRxyHHBvjdROjqjXSbqndfGN6lP3qu//uVHCYcotz0J5R+36huqe0LU9g6siMWsKIKNlEthZ2sqIRWOJLNRRP0Q9+exdKOok68EGpfoG4wtDL37GnYMtnVBrl5p3qC1bFT1gNh3YsL+RDnXHE3UiO+/0betW4PIzf+ANM46MaruGV2Nti76t6X89fKAC1m8iiLYEBuNAkZsCozdGsoNDybuYJnSR5RahtkPNdZulG+/MDcDG5N7GbpU5M/f+/P+TqzBjHaxHjo888yPRAGQn70P9X9vJr4w92uoUybbHqN9lpXLJ0O55F8QIzYNsohFrSimvorKapRcdKO+oa5vdPCQxo+8+lRcFJ9uuHYDRNvbErcB5i4Xa8uD36S5m0ZqA0bLy212JgoH+cjt9i9881nS40RNrTljLvmuKFo2rJTzrjFvSLLCJpE9v4INmz9Bp2DD1KIQ8ABR1XL+zz+I/l/qsB4MEVESRRlsiIYhUO78N0RsqWPZxW4U8ih24xf7/TGz89gFG8aZH0bGLpe6vvb7uCRVFXJ9kmW5nWa7tLXabyfKha13cnxJue7+LBaEUinKYAMAREUFUBb7lsaWDUqT+OOfoVx7L7DJuPROYNONIufNsX8vIYBe0ZHyma6uqt5xDdRzjoJcNN9hB/vZLqJP/4zel8hPyl/Ohzj+nMTtp18C0b8e4i/nRzf0rzfvMH4HKDdOCb6AFFe0wQYAvUmYA0TJK61lQ1EyWwfCrjdm3nfO+9f0AgDITHNdzP48ep53X7F/XToEG5z2SiEilBKIPjap8zceE3194ODo80491b5y2kUoOeMSiN6ZtQ6SN0UdbMTz5jPYIK/8Woba63lKtM+sT61xHlauFXv+nplDKXxsPpNCG+ypDWhes1J/PGxEdspFJkUxG8WR3xduKh7We3S6GT29TiPVAuSIXwGyw/vP/yFxW2WlT+9J5B8xaBjEsWdBVPWAbFwMYRzHUddHf6y1bnCQc04Ud7BRyjEblK6EaCO903idQqsNEvUrZbhTw8anMxM3lnvIjEqURcou+wBI/DiLSptF1coYbORCUXejxL8lcjYKuWFcrtraIpH2zd9bsCG09VF8a41zeP/RWyRuK2fLBuWhHpa1TkqK+zt2rhR3sKGN6F+6KLomRRrkkoVQP37X8/HqJ+9BLlmQ1ntSjhgXQrPeo9Ndq8TrmI14N4o/wYbU1mCxEL36JG6zG4hHFHYVliC5jInpcqGoQzzZZFhsKhIxf3N1Sb3yTACAqKgExm/v7n1nz4J84F+QAEoeeNHze1KOlJUC2jI6lqRXytGnQ731Mog/HOHtnOl2o/jVsjH/B0g1AmGYgitXN0N+NCNx36Eb+fOeRNlUWWV6KmxWWqbgFXfLhikhY2araDrlRsh0XwoR48AyS8uCGLYRlFuegLLXBG/ndBoguvm2EAcdDeXKOyxl0Fo2/FuITb37evPz/9OTIYk9f6e/YM1VQJQPDC0bylmX5bAgxa2oWzbExqP1YX2RCOBx3JD6yrP6kw4PK8d2tHt7IwqHFH296S1QZn+M6FED5feHOZfBz+naX30Cub4FokdPqG+9BMz6UC/HqLEQv/9TdHXMDBOJEeWEcVXvYWydy5XivnoYsyF6XLJbrmyCfO5RfYOXAILBRn7SgomhwyH8+pbv1I3itOBZSeZjNmzHF62MLs8tn37AvH34JhC9ekNwBD/lK+PYOEuXCmVPcQcbxlTRy5Z6O7bFsq5Ep4eWDeb1yE+xm7RyzBn+ndNpgGgsU2gCP7pR7AazNi+HtH4uK6og+jI9ORUQzqjKmaIONozNwup1f/N2cFen6am0PHcr3VkwlAPxcT0+rfhq1dOwxLXTGiSxlg2ZSTeKTauI/HQmsG6NaZty3tXpvwdRCLErMHf4k09Xa4v5eZrBBhqXZF4Wyg4tLvQ6gyQZ48VvoL7GimOLgjaSPpPcMDYta/KT96Deda2+oawcGD4q/fcgCglx+MnRB+O2yW1BihyDDQMvi1vJVSvMG7x0oxhGR6vXn+/+OMotrWUjrYGgDoznMk7Jc2jZ0KakyteeS/89jZ9zY2rnBfPiD5Vbn0hzwCtRuCh7/wHKbU+i5K+X57ooRY3BhtGXH7nfd625ydnTbBRjE3jbekjDRZ5CLN7j5WewYfgTNH6GnLpRvHzObKgfzYCc9UH0iaJA9G9I3Kl/fTRvDFGBENYsopR1DDYM1Hsnud/ZOtaio839sZb+dvn2f90fS7mjtWz42Y1iPNV6fdCxcFiHRJl4TPT1zbby/FayuQnyoVshn7g7uqGk1H50vjWQJiLKEIONdFmTgLWud3+odXDp+nUOe1KoxAPMYFo2xG77AT1rov87qYgGIfLbWd4HF69dZX5eWmq/KBUXJiQinxV1Ui87MhJxl87Wep1vXQ+pqu5GO3/+gfn5V59ASsk+8rDTbu5e1zNJxvA7FyNGQ9w80ZQ6PEHEEOQ2Lwf6DXT/XtYpryUlwKrmxP3sFmEjIsoAWzYs1HuuT70TgIRoQ6pAe2va7ys/tFmLgsJFCzZ87UYx/AlWVScPNABzo4rXXBvW6bIlpRBb7RR/b+WSf0FstzuUI/7i7bxERCmwZcPqq0/c7afaNGGvbwGqeyY9zCk/gnzp/4Cd9nL33pQbAeTZEMI47tTjeb2uNNtuGVdUVg4xcgyUKyYD/QZCVFZDnOwx3wwRkQtF37KhnJnuwjw2wYabREvN0bTQKK8ARo7Rt1v70/NU5K7rELn2b5Ae07/nBW1cjq9TXxX7x84HGMrT4rybDWlNk78iuuqxGDIcorLa07mIiLzwrWXj4Ycfxquvvhp/PnDgQNxxxx1JjggHscW26R1oNzjPrrXDKrYGBfr0h9h1P8gfYyvAdnZCnfFfyE9mQjnzHxA9kreQhJFsbdGnDzctMyWpyndyveHG7mfWV2Pg4qZ7xvC5UKc+hJKLb3L/Xj9/b37O6a1ElCW+BRs///wzLrroImyyySYAACWf0sLW9QVW2wyUS8buhiOTf5uXqgp18pXRJ336QWy/G+TDt+qvP3Vf9P/XnoX447HeyhMGTY3643z6/bthnDHkZ6uNMcBw02JizBdgDR5SkG+9ZN7glMuDiMhnvtwRIpEIFi1ahDFjxqBHjx7o0aMHqqryZ3U9sf8fvR+UTsvGz9/rg/pWNkEoJRCHHp+4X4aJm3Km3dBM7+cS6GEQ2EwhY7CR+s9RpLlEtu1YIb9WriUiSsGXlo2FCxdCSonzzz8fK1euxJgxY3DKKaegX79+tvt3dXWhy7C2gxAiHpz4Of1TO1eqcyp7/wER49La3d3xJbXl6pWQn7wHsdPeED0Ts9CJ0VtEM4C2rodIMn1VrlwBOf3f+obGJRBCQFRWJ47+WN2c1s/BbX2DYhynISKRwMuR1foap6gK4dt7SkMLkCgpSXleUVsH7acsdtrLVTlkeyvUM/+UsL3kqFNDP90615/pbGN9C1ux1dfIl2Bj8eLFGDRoEE444QTU1NTg0UcfxX333YdLL73Udv/nn38e06ZNiz8fPnw4Jk2ahP79g2nWra9P/Q1ukeFx6b3XQ3a0Y8CND2DxXw4EVBVVjQvR70J9Wuzq6mqsA9BzzOZoXbEMkdb16Nu3DyoabNI/A2i8/u+I/KQ3e1dstiUGNDRg/YABWGnZV876EA0O53HDTX2D0LbkZ2grxvSt6+X4s/BbNurbXQIsjT3u168fyn2qW9vivvGf2YCBA1E6MPV51x5zGtY8fg+qa2rRx0U5Wt9/E9ZOwqH//cx7YXMoV5/pXGF9C1ux1RfwKdjYddddseuuu8afn3TSSTjjjDPQ2tqK6urEUe4TJ07EhAkT4s+1KK+pqQndNitSpksIgfr6ejQ2NqbMtqj86SSozzwIAOj46lMAwNIPZ8anF7a99waW/vmv8f0jLdEBgy3rW+Pnbm5qgqhZCjvdP5n71zsjESxduhTqevs050uX2p8nGS/1DYLa1BR/3NzYCFHTJ9D3y2Z9ZfPy+OMVy5dDVPTw5bzqKn0W0vKmJgg1RcuGEKiO5eJobWlBh4vPidpsDmfF7w5N6/OVC7n+TGcb61vYCrG+paWlrhoKAsmzUVtbCyklVq9ebRtslJWVocwuTTIQyC9ASpnyvOI3BwBv/AdYqa/mKlebL9Kmc8RXAEW8iV1GIgljOeSqZsipDyW+335/jJbLYWl6NdKdOsGTAzf1DYQhUJTdXf7O2kgiG/WVhrEVUkof62Y4L4S782oZbiORlPWWTY1Q778x4T3z7UKXs890jrC+ha3Y6gv4NED08ccfx/vvvx9//sMPP0AIgb59+/px+uyp7W16asom2scy/sS4ToY2sM/w4ZGRCKSqQk57BPKz983HjtgUYmxsIa21q+3LMudLT0W3kj9/D/XZRyE7szfYVBrX1PCxhSp0/JxpY5yN4jIzaTwlvoukXurd19m8Z4HNFCKi0POlZWODDTbA008/jV69ekFVVTz88MPYfffdUVFhv3JlaCXJbSE2HuPwgtAv3rHWDqlGoF59NlBaZntDEAMM+SeqHJIpZRj0qtefHz3NZ+9DOfE8iI1HZ3ZCN4zpswtuMS/DL2TQMP9O6zmpF/SWDetigHYW/5K4jcEGEWWZL1ed3XbbDTvttBNuvvlmTJ48GePHj8cJJ5zgx6mzSvRInG0SZ71AazMvFEWfqaAFFuvWAr8uBBb+BLSsTTyXIQgTDUNt3069/SrTOAGryD03IHLyAalbLlYsgzrpwuT7+MUQYMjZs2x3kd98hsh5x0B97fnslMkvWqtVWXlwI8ndnjf2WTTO/lEfvQORkw+A+vBtro8nIsoW3646Rx55JB555BE8/PDDOP7441FZmYfZCZOsayLXrjFv0AILpUS/+M//MTpuw/itvmdt4rmWLo4/TtbiIN991fE1zIquHKuecahpc/fyRnSfcVjiuRqXOJ/LL4ZcDvKd6dGMohbq9H8D69ZATpsSfHn8FF/x1efzGgMMly0b1m4UKSXk+29EH3/4duoTMNggoizjVccoWYrwOV+Yn8eDDSV+k5DPPQr59APmhFZ23QmW1g7HbKHNTfbbLYwDjVZPmQx0JM5wkb/84OpcGem0DHb95cfEfQxLmss1ebQeTBDLyxvPC7hfTVYbOKx9BrWU9/FTRs9pF+xFj+efPRFlF686RtXJpzOauiy08QklJaabhHxnOmBIWBZfvMtAOeAI03Ox/x+hXHozoC33HT+ZfZ98wijmb2fFt3V+P8fmCABKSbTVJUjWmTWWTKhyVTNg6BpSHw3/2jlxxgHBQfHYjQJVhezqgnrvDebXtZWLIw5jOvwOmIiIUuBVx6isPOnL8p1X9CemMRuWH6OxZWONJcfB8edAWIIKIQTEhiNRctpFsLxgXxDLTV2dfBXkA/8CAESW2XeXyBnToZ55GGSGs1ySsrRsJKwyOt+ylsfCn4Iri+8C6kYxctniILQBomoE8n9vAOvMXXzqXddCrl2lt6oJBWKXfTy/DxGRX3jVMepKvp6H/PfD+hOtlcAwZkM/j33uDABQdtoreRnqh+jv5zSjw3JzAQD56czk5503B+jugnrr5Yhc8hfIWR8m3z8dXZbBqp3mYEO2rDO/nk/dKGpA3ShGLs8ttWnF334BrLLmn43t8/mH5tY3Y2sYWzaIKMt41TFyMahVS/QV/9ZeUQnMt4yHaLd8owcg9psI5ZKbU55f7L6f/sRhQTan1glpl3dhk3GJ25oazTlE/GINsrq6oL78NNT7b4rmHHn8Lv/fM2uy0LLhshul7aN39Sc243MAAOvX6r+PklLI5b+aXyMiyiIGG0al9llNTbTAoj12ka9MzJOhPv+Y6bn4zYFQDjkeYvjI1Oc3rhzbljjeA0DiQEzNL/MSNonRWzi+lUzRkuOZNThSVcgXnoq2uvw0174M+bLCrbElKyheZ6PAZtl4Tet6oCmWkrzfAGC5IT15Rf6syExEhYHBhlGJixxnWtdGLNgQlZVAL8saIAt/Nj+3WS3WiTC2RLS12u/UbR9sRJ55IHFjnXMWV/n4na7LlYpcND9x2qVxQK2UwEibxGgtiV1CoRQPLn2+UZu6N1xmEC13kSyvvQ1yaXR5QdEw1NxllWIgNBGR3xhsGAgXLRvqu69GWwTi3ShVwIhNkh/k5uaglWGDERBHnRp9YjOTBYDjmBBhE1gIuxt8jPxwhutyJSOljGZMtTKWv7zcnE1Vu7Fa85eEVUdAwYaRy6mvFeO3sz/83Kvjj+XM1yH/Hctl0jAE2HKH+GtiQxctbEREPmKwYVTqomVj7teQb75o/qZrnXVh5SHYAAAxenz0wepm+64Oh+4P2bzMvGGL7SAGBL/Mu5ZQKmH7d18aniDeFaGceRmg3fBWrUg4LpS033dFgMnqXHaj9Njr94kbt9oJYsx4iMNPTnytYRiUY8+C2PsPECf/HWLo8AwLSkTkDYMNI4duFOXCSabncu7Xhm+6lUBZimBipbvkXHGGZm752nOJr3c7jLWI9cuLLbaDcv8LKDnzH8nfZ8iG3srlQMaymSZYND/+UL1vkp7kq6QEot/A6LG/LvClDEGT2qBf31s29G4Ut2nQhc2KyfHpsDYrSYqGIRA9aqAcfjKU7XZLr5hERBlgsGFk05ctJhyemFK8slJvzaissr34G3lOFW5YnE1+8l7i605Ta7Vui4pK041L/OEI+/2dxoR45bRyrZFxnZe+/SHXR7Nbyhee8qcMQdN+5qkCy1zRBo0aZ51oBg5K3EZElEUMNoyGj4LYdV+Ig46Gcu29UO6cCuXAIwEA4sCjzPtq3yArqlI2rYvy5MnCEvY3jh2xG7ehdaOM2dL+BJbyiD8cDuXsKxL3cxoT4pV1MbjNt02+f796+5timGm/b7cpxV2f16fzxGbJiAmHJ7wkUiSrIyIKGoMNAyEElD+fCeX3h0EMGARhuGmLnfbWd9QSYlX3iI7HSDXWwy7XhVu9zYM+ZUd7fIyE2Gw8lPOvB/oOMB9jDTaEgBi7NcSh0ZV4lb/GAo+29aaVQ9NmCTZEsjVmEO0GUI77a/y5TJIELTSyka7cA+XE8ywbouUStXVQbtQXuRN/PjObxSIissVgwyXRp58puycAYNCwaHdFioF9YuffeH+/7XaP/m95T/n1Z/qT0jKIUZsBG25sPtjhm6yy70EoeeBFYPTm+kY/ulKsLRtuphAPMQxSnPNV5mUIXEBJvWzGWLghxm1jOY/htd59IXbdF6jrm5Aan4goFxhseKBYRvqLQRvEXnD4MZaVQ7n/BdcD/0w23hQAIL//BpHJV+rL0jfqy9NrLRjK4X8xH1uSPPGUKC3TZ8isd1gZ1It2SxZLpwGsAJR/3BJ9YBiXgl51mZchaLGgQISkZUNYcrfIxfNNz5U/nwll0kMpW5mIiLKBwYYXG1tyVgwaFv2/pld8k9jjt/rrPWvTCzQAPZvpqhXA7FlQLz89+ny9vr5IvJuntpfpUOGmZaE6dhNyylLqRbdlDRdrC5BRLNeGUBSgti7z984WreXA70XMPE6LNlLOuFR/Yk0kB3OmUSKiXHJxV6K48vJoMirtW25d7+j/vzkAcv4PEFvuYL7oZ5Kp0eYmJBfN19NmA/GWDaGURFszjAtvpVJVDaxuzniQqFxmGOg5eAMol90GfPWJ87hHY2IvLViyBithJB2Wa8/UyDEQO+wJ1A/2fKgYv30ABSIi8h+DDQ+EIdAAANnZCYFoC4OW0yLy5bXx15WTzrOewv17lZUl3LDVq8+G2M2wUJtxjQtjEOKmZUPbx27xNg/kjP/GHytnXwlRUgK5+TbOBxjfTytDxIdBqoHTxmz4240ihIA48dzMT8RWDCIKMV6hMiBspngaszOKIRlkanTI5yCNa1xsNMr+WDeZUGNrkqi3XQG5cgWkw0JpqcgVhqylsZYcUVoG5Z/3mnes6xv99m5cjE4rZyRxjIdcshDSuHhYrsUjv3CM2UjQu1+uS0BE5IgtG+kasqHt4Dux70GAqkJss3Nm53fKzfHVJ9H3OfR457Vc3Kzmunpl/KF6YWxK7KU3e183I1YeAKauHzFwEJSbHoF8ZzrELvtEp+dKaR5HEGvZUP/9CMRmX0E55DgAgFzfAvXK6JRN5e5nUyZNywoZ0GwUvzQMzXUJiIgcsWUjXQ7fJEVlNZSDjs6sVQMwDTq1lSRRk7bap1dyzpfe9reMtbAOhhV1faI/i34Do90F1qZ+bWzJ4vmQrz0HuS62KJthvRT57iueyhQcLdgI15+McsnNEDvuBeVY5tMgovAK15UzD4jj/goMHxXPLBqYVM3ilnEZ4qCj9cejt0jvPev6eNs/za6XOGvwoaWAN97QVzcnHKa+9xrkd1nOzRHSlg0xfCSUE86xXfGXiCgs2I3ikbLzb4A0knR5JaqqgfHbA19+bL+DZVyG+O3BEL37oa53HdZsOj7l+ZXbnoR6jiUFu9dBhhln/rTcude3AP0Gmsth6O4BAHXm65CP3wUJQDzwYobv70HIMogSEeUTtmyEWIkxj0LCi5ZgQymBsvPe6LH3hOhU2BREjxqI7Xc3b/Q6BdXn6aDq43fFHujnlbNn6Y+lhHzsTv0A4+DUoMVjDQYbREReMdgIu9i6KuKkv5k2Ow4O9UBsa1lu3GuwoWa4ipj1vr1gXvT/iKEcxuyklvJFbrwos/f3JJzdKERE+YDdKCGnnHt1NPHWyiZz3g0301tTGbe1+Xkkyy0b1lYCLfOocXG4SDdk63qI6h5Al2UNllWJ4zkCI8M5QJSIKB/wyhlyoqQEoqY2cRl7H9a8EIoC5a+X6xs8t2zowYbYb2I6JTA/22K76ANrki8tqLAu+DYoi9M9ZTBJvYiIigGDjXxRacgWusk4YMRoX04rxm0TzYMBJF1AzY7Uuj0AiIOPS+PNzTduOf8HyLlfJ7awdMcGoi4zJ/kSA72n+E6HbGuF/OS9rLwXEVEhYjdKvqg0tGy0tqS/wJudeCZPby0bcvq/4499Kc8Ps6He/I+Ezeo/z4Py18uh/ucJ8wudmc6GcUd9ZDLw68LokyT5TYiIyB6DjXxRbgg2evX299zazBaPLRsZ8xCgqLdfnbBNWrtVgjLrQ/2xDwNziYiKDYONPGHMvqkcfbq/J9daNlyO2ZBzvoD8fnb0uExWbM20NcQ6YDQbwpA6nYgozzDYyCPKTY8A7a0QfQf4e2Lt27rLwEG99QrTc3GMz8GPW22t2X9PtmwQEXnGYCOPiLo+ADymFHfDw5gNub4lYZtoGOZ3iZKr6xPNLLpqBaTMMNeHVxyzQUTkGWejEFDioWXDbtn3VIvGOUmzG0VstVP02M5OqGtXp/fe6WLLBhGRZww2yNOYDfnBW4kb0x7HkCLYGLdN4rbRW0D88c9Az1oAQCSbib0AjtkgIkoDgw2KBxsykno2inxneuLGkjR741LEGqK8Asqt5umuyrFnQVRUAlU9ouWx6dZxS3Z3e++GYcsGEZFnDDZIDxaWLEjv+HS/7afoRhH7/xGiZy3Efn/UN2pTgKuqAQDq+nVpvbVsbYF60YlQ75vk6TjRvz6t9yMiKmYcIEpAV6xFo3EJZOMSiHr7zJxyyUL749Nt2TA2bfTpD6xsAkZvAeWPfwY6OiA2HBnda9NxkK89F91PS24WG6gp01jmXi6aD/n2y8CaVcDnH3g7eECD5/cjIip2DDYIMCzxpt51LUquuTtxD1WFeuWZ9oenuyicYW0V5ewrIL/+FGLn30BYB5yOGQ9x6PEQ/RsgLLNBpMcsorK7G+rVZ5uL8cqzkD9+C+X0ixNW05XWYIRjNoiIPGOwQRC9euvhRsMQ+51a1jifIN2WDeNU274DoOx/sO1uQimB2Ney0Nu8OQCAlf+6DKUPvuT+PZuXJ2ySzz0affDlx1Bnz4LYeieI2OBU9d4bzDtzzAYRkWccs0HAljvGH4rYLI8ETcscD097XRRjevQMbuLSS5r1lU3O5/n6U8j/vQn19qttU6GL/Q+GKK9Ip4hEREWNwQZFl7E/8hQAgHQYcCl/+s52u3LH0+m/sWGqrSgpSfs08vX/uN930c/Or304Q3/84v8lvK4cfKynchERURSDDYrqURP9v8Vhdofxm/6AQVDunIqSB16EqKxO/z19WvhNfvuF+50N40SSnvO156B++j5QWxfdUFnlvWBERASAYzYoJj5uY/VK+x0ikeh+e/wO4shT/FlS3uOS9k7kQvvWCqmq0em8g4dBKLGWEw95OeT9N8YfK2ddllEZiYiKGVs2KCqWJAsd7ZBSQjY1mrtUtC6P0jJ/Ag3jOTPVtt52s3z5aahXnw3570f0ba8+m957cE0UIqK0BRJsXHvttXjnnXeCODUFRbuZdnVCveYcqJf8Beo5R0F96t7o9vbYCqt+DpB02aWRLvlSdDyJfPOF6Nu9+6r+4pjx3k7GKa9ERGnzPdiYOXMmvvrqK79PS0ErjwUbrS3AovnxzXLGdMi1q/TuhxqH2Sq51Ke//fa+A0xP5RN6/hCx4SjzvpuMS/4epWzZICJKl6/BRktLCx577DEMGjTIz9NSNiT75t60TM/UWR7Cm+7KpmjSsRnTETn5AMglCyA7Okw5NaxroIixW5mfb7+7/mTcNhB/tiQwYzcKEVHafB0g+thjj2G77bZDZ4qsjl1dXejq0mciCCFQVVUVf+wX7Vx+njPMMqpvmXP3iHrDBRBjxkMCEOWVgfw8Mz7na89BPvcYAEC98iwoR55qelm97u/m9xtoDohFbV08sZkoK0PJbvtBbjIOkUujU4JFeXkoPkf8TBc21rewFVt9jXwLNmbPno1vvvkGt9xyCx5++OGk+z7//POYNm1a/Pnw4cMxadIk9O/v0Byeofr64lo8K936LkrympzzJQCg98CBqG7wZ32QX0tKEIk9bvB4TmtZ1VigoSn/aQ7ajRt++TH+cPBz7wPd3VhieLnv0GHQ0n3Vn3ERSusbEKmuwq/atsFDoPSs8VTGIPEzXdhY38JWbPUFfAo2Ojs78cADD+Dkk0+Ot1AkM3HiREyYMCH+XIvympqa0O3XDIXYeevr69HY2Oh9KfE8lGl9lTMuhXrXtUn3WS1KsWbp0nSLaBIxTH1d6tM5Ne1LFzu+tmzlKsi2VtO2lWvXoeS2J4HuLjRJBYiVR+x7ECAllq1rAdalv5y9X/iZLmysb2ErxPqWlpa6aijwJdh49tlnMWLECGy11VapdwZQVlaGMocxAkH8AqSUBfOLdSPt+moJrACIHfaA/Ogd8+vjtoHcYGPAp5+l2HJHyLdeAgYM8l7eml7AuiTrtdikGwcA5YYHoz8fmN9PllVAxBKbGcuiHHpCwrYw4Ge6sLG+ha3Y6gv4FGy8//77WLt2LY477jgAQEdHBz788EPMmzcPJ510kh9vQdlQ3VN/3LNXwsti7Fb+jqn545+BwRvEFz3zQrnsNmDOF1AfuT3xxbFbRafoNi4xby8phdBmqAjL2OgwDnwlIioQvgQbV199NSKRSPz5448/jpEjR2KPPfbw4/SULZWV+uMSm4lK6S4l70CUV0Dsum96x/buC7HLPuhVU4NVd1i6fjra41NVxVGnQey6L+RbL0FsMtZ4BvMxXGCNiCgwvtw9+vbta3peWVmJ2tpa1NaGMCcDOTPccMWYLSHffDGephxA+kvJB6i0YYj+ZNRY4IfZwI9z9G2VVdGF5vY9yHygtYGGwQYRUWACuXucccYZQZyWglZuaNmIRIC6vqZcFWEMNirGbQ2x455AZyfEb/4AddJF5h1WLLM/0Ngd1DAUoiqDBeWIiCgpro1CccLYTdK7L5QTzjXvkMEy8EERioKSE8+DcuqFtq0TYsORTgfGHyoXTgqqeEREBK76ShbK+ddDrm6GGLJh9Pk9z0I97eDoi2scVoQNi0EbJG7bbEvbXUVpKZTzrgEiEYgePW33ISIifzDYIBMxajPTcAZRapiiXB3um7KwGcCabPaMGL1FkMUhIqIYdqNQSuKkv0Hs8VuI7ffIdVFSSljThIiIco4tG5SSsv3ugHGhshATG22C4kqVQ0QUfmzZoMJS1zf1PkRElFUMNqigiB49oZxxCTB6CyiXT851cYiICOxGoQIkxu+AkvE75LoYREQUw5YNIiIiChSDDSIiIgoUgw0iIiIKFIMNIiIiChSDDSIiIgoUgw0iIiIKFIMNIiIiChSDDSIiIgoUgw0iIiIKFIMNIiIiChSDDSIiIgoUgw0iIiIKFIMNIiIiClSoVn0tLQ2mOEGdN6xY38JXbHVmfQsb65u/3NZFSCllwGUhIiKiIlbQ3ShtbW248MIL0dbWluuiZAXrW/iKrc6sb2FjfYtHQQcbUkrMnz8fxdJ4w/oWvmKrM+tb2Fjf4lHQwQYRERHlHoMNIiIiClRBBxtlZWU45JBDUFZWluuiZAXrW/iKrc6sb2FjfYsHZ6MQERFRoAq6ZYOIiIhyj8EGERERBYrBBhEREQWKwQYREREFKq8StH/66ad49NFHsWLFCgwdOhRnn302hgwZgoULF+Kee+5BY2Mj9tprLxx99NEQQsSPa2xsxMUXX4wpU6aYzjdnzhw88MADWLt2LSZOnIgJEyZku0op+V3nN998E1OnTsW6deswatQonHPOOejdu3e2q+XI7/pquru7ceGFF+KEE07AZpttlq3qpBRUfW+99Vb06tULJ5xwQraq4orf9Z02bRpee+01tLe3Y+zYsTjttNNQW1ub7WollU6dk/2dhv265Xd9C/Ga5aZOYb1mpStvWjYaGxtx991348gjj8S9996LhoYG3Hfffejq6sKkSZMwfPhwXH/99Vi8eDHeeeed+HHLli3D9ddfj/Xr15vOt3btWkyaNAk777wz/vnPf2LmzJmYPXt2lmuVnN91njt3Lp555hmceeaZuPPOOwEAjz/+eDarlJTf9TV68cUXsWjRoizUwr2g6jtr1izMmTMHf/rTn7JUE3f8ru+cOXPw4Ycf4qqrrsJNN90EVVXx2GOPZblWyaVT52R/p2G/bvld30K8ZrmtUxivWZnIm2BjyZIlOOqoo7DTTjuhrq4O++67L+bPn48vvvgCra2tOPbYY1FfX48jjjgCb7/9dvy4SZMmYe+9904438yZM9GnTx8cfPDBaGhowCGHHGI6Lgz8rvPSpUtx8sknY/PNN0ffvn2xxx57YP78+dmsUlJ+11ezdOlSvPTSS+jfv382quFaEPVtb2/HQw89hCOOOAI9evTIVlVc8bu+8+bNw5ZbbolBgwahvr4eu+yyCxobG7NZpZTSqXOyv9OwX7f8rm8hXrPc1Cms16xM5E03ytZbb216/uuvv6KhoQELFizAqFGjUFFRAQDYYIMNsHjx4vh+F110EQDgiSeeMB2/YMECbLbZZvFmrY033hhPPfVUkFXwzO8677nnnrbnCwu/66u5//77ceCBB+LLL78MpuBpCqK+06ZNQ3d3N0pKSvD1119j7NixUJRwfKfwu75Dhw7FlClTsM8++6CyshJvv/02Nt9884Br4U06dU72dxr265bf9S3Ea5abOoX1mpWJcFyFPOru7sbLL7+MffbZB21tbaboTwgBRVHQ0tICABgwYIDtOVpbW02vVVVVYeXKlcEWPAN+1NmopaUFb775JvbZZ5/AypwJv+o7Y8YMtLa24oADDgi8zJnwo75NTU2YPn06BgwYgGXLluHJJ5+Mdy+EjR/13XLLLTFw4ECcddZZOPnkk9He3o6DDjooG8VPi5c6a6x/p/l03fKjvm5fCwO/6psv1yyv8jLYmDp1KioqKrDXXntBUZSE1K/l5eXo7OxMeo6SkhKUluoNO26OySU/6mz04IMPYtSoUdhyyy39Lqov/Kjv2rVr8dRTT+G0004Lzbd7J37U991330WvXr1w2WWX4bDDDsOVV16JuXPn4ptvvgmy6Gnxo74fffQRVqxYgVtuuQUPPvgghg4dittvvz3IYmcknTpb/07z6brlR33dvhYGftQ3n65ZXuVdbWbPno3XXnsNZ599NkpLS9GzZ0+sXbvWtE9bW5vpD9KO9Tg3x+SKX3XWvPPOO/j2229x2mmnBVHcjPlV30ceeQR77bUXNtxwwwBLmzm/6tvc3Ixx48ahvLwcQPRbb0NDQ+jGMfhV35kzZ2LffffFkCFDUFtbi+OOOw6ffPJJ0oHCuZJOne3+TvPluuVXfd28FgZ+1TdfrlnpyKtgY/ny5Zg8eTJOPPFEDBkyBEC0z/KHH34w7dPV1YWePXsmPdeIESPw448/xp/Pnz8fffr0CabgGfCzzgDw008/YcqUKTjnnHNQV1cXVLHT5md933//fbz66qs47rjjcNxxx2Hu3Lm44YYb8J///CfIKnjiZ3379u1r+uakqiqam5tD9bn2s75SSqxZsyb+fPXq1QAQum6jdOrs9HeaD9ctP+ub6rUw8LO++XDNSlf4QmIHnZ2duOGGG7DNNttgu+22Q3t7OwBg0003RVtbG2bMmIE999wTzz33HMaNG5eyCWqbbbbBQw89hK+//hpjxozBiy++iC222CIbVXHN7zqvWbMGkyZNwgEHHIARI0bEz1dZWRl4Xdzwu77atDLN5MmT8bvf/Q7jx48Pqgqe+F3fHXbYARdffDE++ugjjBw5Eq+88goikQjGjRuXjeqk5Hd9N910U7z00kvo06cPysvLMX36dGyyySaoqanJRnVcSafOyf5Ow37d8ru+hXjNSlansF+zMpE3q75++umnuOmmmxK233nnnVi4cCEmT56M8vJyCCFw5ZVXxiNMIBpVnnnmmZg6darp2Ndffx1TpkxBZWUlevTogX/+85+hipz9rvP06dPxyCOPJJzP+nPJlSB+x0ZXXnklDj300NAkyAmivp999hmeeeYZ/Prrr6ivr8cpp5yCUaNGBV4XN/yub1dXF5544gl89NFH8eRIp59+uqsB0tmSTp1T/Z2G+brld30L8ZrlpU5hu2ZlIm+CjVRWr16Nn3/+GSNHjvT0zWb58uVYsmQJRo8eHZpo2a1065yvWN/CVmz1BYrvulVsv+Niq28yBRNsEBERUTjl1QBRIiIiyj8MNoiIiChQDDaIiIgoUAw2iIiIKFAMNoiIiChQDDaIiIgoUAw2iIiIKFAMNoiIiChQDDaIiIgoUP8PB9QtC7jZDTsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./stock601939.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3597 entries, 0 to 3596\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3597 non-null   datetime64[ns]\n",
      " 1   open        3597 non-null   float64       \n",
      " 2   high        3597 non-null   float64       \n",
      " 3   low         3597 non-null   float64       \n",
      " 4   close       3597 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 140.6 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3592</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.454</td>\n",
       "      <td>8.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3593</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.490</td>\n",
       "      <td>8.389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3594</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.524</td>\n",
       "      <td>8.426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3595</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.582</td>\n",
       "      <td>8.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3596</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.648</td>\n",
       "      <td>8.534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date  open  high   low  close      5     10\n",
       "3592 2024-12-19  8.52  8.58  8.40   8.48  8.454  8.368\n",
       "3593 2024-12-20  8.47  8.60  8.46   8.48  8.490  8.389\n",
       "3594 2024-12-23  8.45  8.73  8.45   8.62  8.524  8.426\n",
       "3595 2024-12-24  8.60  8.78  8.59   8.78  8.582  8.467\n",
       "3596 2024-12-25  8.78  9.02  8.77   8.88  8.648  8.534"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>19358.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-02</th>\n",
       "      <td>2</td>\n",
       "      <td>19359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-03</th>\n",
       "      <td>3</td>\n",
       "      <td>19360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-04</th>\n",
       "      <td>4</td>\n",
       "      <td>19361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-05</th>\n",
       "      <td>5</td>\n",
       "      <td>19362.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            value     date\n",
       "index                     \n",
       "2023-01-01      1  19358.0\n",
       "2023-01-02      2  19359.0\n",
       "2023-01-03      3  19360.0\n",
       "2023-01-04      4  19361.0\n",
       "2023-01-05      5  19362.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date  open  high   low  close\n",
      "0    2010-01-04  6.21  6.27  6.12   6.12\n",
      "1    2010-01-05  6.15  6.26  6.08   6.21\n",
      "2    2010-01-06  6.20  6.23  6.10   6.11\n",
      "3    2010-01-07  6.11  6.15  6.01   6.02\n",
      "4    2010-01-08  6.01  6.08  5.99   6.04\n",
      "...         ...   ...   ...   ...    ...\n",
      "3622 2024-12-19  8.52  8.58  8.40   8.48\n",
      "3623 2024-12-20  8.47  8.60  8.46   8.48\n",
      "3624 2024-12-23  8.45  8.73  8.45   8.62\n",
      "3625 2024-12-24  8.60  8.78  8.59   8.78\n",
      "3626 2024-12-25  8.78  9.02  8.77   8.88\n",
      "\n",
      "[3627 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3627 entries, 0 to 3626\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3627 non-null   datetime64[ns]\n",
      " 1   open        3627 non-null   float64       \n",
      " 2   high        3627 non-null   float64       \n",
      " 3   low         3627 non-null   float64       \n",
      " 4   close       3627 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 141.8 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.10</td>\n",
       "      <td>6.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.04</td>\n",
       "      <td>6.100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3622</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.454</td>\n",
       "      <td>8.368</td>\n",
       "      <td>8.106333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.490</td>\n",
       "      <td>8.389</td>\n",
       "      <td>8.122333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3624</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.524</td>\n",
       "      <td>8.426</td>\n",
       "      <td>8.146333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3625</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.582</td>\n",
       "      <td>8.467</td>\n",
       "      <td>8.177333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3626</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.648</td>\n",
       "      <td>8.534</td>\n",
       "      <td>8.211000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3627 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date  open  high   low  close      5     10        30\n",
       "0    2010-01-04  6.21  6.27  6.12   6.12    NaN    NaN       NaN\n",
       "1    2010-01-05  6.15  6.26  6.08   6.21    NaN    NaN       NaN\n",
       "2    2010-01-06  6.20  6.23  6.10   6.11    NaN    NaN       NaN\n",
       "3    2010-01-07  6.11  6.15  6.01   6.02    NaN    NaN       NaN\n",
       "4    2010-01-08  6.01  6.08  5.99   6.04  6.100    NaN       NaN\n",
       "...         ...   ...   ...   ...    ...    ...    ...       ...\n",
       "3622 2024-12-19  8.52  8.58  8.40   8.48  8.454  8.368  8.106333\n",
       "3623 2024-12-20  8.47  8.60  8.46   8.48  8.490  8.389  8.122333\n",
       "3624 2024-12-23  8.45  8.73  8.45   8.62  8.524  8.426  8.146333\n",
       "3625 2024-12-24  8.60  8.78  8.59   8.78  8.582  8.467  8.177333\n",
       "3626 2024-12-25  8.78  9.02  8.77   8.88  8.648  8.534  8.211000\n",
       "\n",
       "[3627 rows x 8 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0           8.78   9.02\n",
       "1           8.60   8.78\n",
       "2           8.45   8.73\n",
       "3           8.47   8.60\n",
       "4           8.52   8.58\n",
       "...          ...    ...\n",
       "3622        6.01   6.08\n",
       "3623        6.11   6.15\n",
       "3624        6.20   6.23\n",
       "3625        6.15   6.26\n",
       "3626        6.21   6.27\n",
       "\n",
       "[3627 rows x 2 columns]>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.12],\n",
       "       [6.21],\n",
       "       [6.11],\n",
       "       ...,\n",
       "       [8.62],\n",
       "       [8.78],\n",
       "       [8.88]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14613.0</th>\n",
       "      <td>6.21</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14614.0</th>\n",
       "      <td>6.15</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14615.0</th>\n",
       "      <td>6.20</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.10</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14616.0</th>\n",
       "      <td>6.11</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14617.0</th>\n",
       "      <td>6.01</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3627 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         open  high   low  close\n",
       "date                            \n",
       "14613.0  6.21  6.27  6.12   6.12\n",
       "14614.0  6.15  6.26  6.08   6.21\n",
       "14615.0  6.20  6.23  6.10   6.11\n",
       "14616.0  6.11  6.15  6.01   6.02\n",
       "14617.0  6.01  6.08  5.99   6.04\n",
       "...       ...   ...   ...    ...\n",
       "20076.0  8.52  8.58  8.40   8.48\n",
       "20077.0  8.47  8.60  8.46   8.48\n",
       "20080.0  8.45  8.73  8.45   8.62\n",
       "20081.0  8.60  8.78  8.59   8.78\n",
       "20082.0  8.78  9.02  8.77   8.88\n",
       "\n",
       "[3627 rows x 4 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14613.0</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14614.0</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14615.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.10</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14616.0</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14617.0</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3622</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3624</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3625</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3626</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3627 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  open  high   low  close\n",
       "0     14613.0  6.21  6.27  6.12   6.12\n",
       "1     14614.0  6.15  6.26  6.08   6.21\n",
       "2     14615.0  6.20  6.23  6.10   6.11\n",
       "3     14616.0  6.11  6.15  6.01   6.02\n",
       "4     14617.0  6.01  6.08  5.99   6.04\n",
       "...       ...   ...   ...   ...    ...\n",
       "3622  20076.0  8.52  8.58  8.40   8.48\n",
       "3623  20077.0  8.47  8.60  8.46   8.48\n",
       "3624  20080.0  8.45  8.73  8.45   8.62\n",
       "3625  20081.0  8.60  8.78  8.59   8.78\n",
       "3626  20082.0  8.78  9.02  8.77   8.88\n",
       "\n",
       "[3627 rows x 5 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x29a203744f0>]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python\\lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.0768 - val_loss: 0.0922\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0153 - val_loss: 8.9926e-04\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0010 - val_loss: 0.0013\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.2371e-04 - val_loss: 0.0013\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.8106e-04 - val_loss: 0.0012\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.5974e-04 - val_loss: 0.0011\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.4958e-04 - val_loss: 0.0011\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.4475e-04 - val_loss: 0.0011\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.4142e-04 - val_loss: 0.0011\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.3949e-04 - val_loss: 0.0011\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.3749e-04 - val_loss: 0.0011\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.3628e-04 - val_loss: 0.0011\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.3351e-04 - val_loss: 0.0011\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.3064e-04 - val_loss: 0.0011\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.2866e-04 - val_loss: 0.0011\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.2480e-04 - val_loss: 0.0011\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.1976e-04 - val_loss: 0.0011\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.1359e-04 - val_loss: 0.0010\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.0728e-04 - val_loss: 0.0010\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.0052e-04 - val_loss: 0.0010\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.9439e-04 - val_loss: 0.0010\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.8859e-04 - val_loss: 0.0010\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.8228e-04 - val_loss: 9.9751e-04\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.7609e-04 - val_loss: 9.8499e-04\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.6919e-04 - val_loss: 9.7468e-04\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.6236e-04 - val_loss: 9.6148e-04\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.5506e-04 - val_loss: 9.4909e-04\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.4756e-04 - val_loss: 9.3173e-04\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.3848e-04 - val_loss: 9.1228e-04\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.2974e-04 - val_loss: 8.8885e-04\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.03232\n",
      "均方根误差: 0.17978\n",
      "平均绝对误差: 0.14227\n",
      "R2: 0.90678\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
